Skip to main content
SpringerLink
Log in

Adversarial dual autoencoders for trust-aware recommendation

  • S.I. : Deep Social Computing
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

Recommender systems face longstanding challenges in gaining users’ trust due to the unreliable information caused by profile injection or human misbehavior. Traditional solutions to those challenges focus on leveraging users’ social relationships for inferring the user preference, i.e., recommending items according to the preference by user’s trusted friends; or adding random noise to the input to improve the robustness of the recommender systems. However, such approaches cannot defend the real-world noises like fake ratings. The recommender model is generally built upon all the user-item interactions, which incorporates the information from fake ratings or spammer groups, that neglects the reliability of the ratings. To address the above challenges, we propose an adversarial training approach in this work. In details, our approach includes two components: a predictor that infers the user preference; and a discriminator that enforces cohort rating patterns. In particular, the predictor applies an encoder-decoder structure to learn the shared latent information from sparse users’ ratings and trust relationships; the discriminator enforces the predictor to provide ratings as coherent with the cohort rating patterns. Our extensive experiments on three real-world datasets show the advantages of our approach over several competitive baselines.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

Notes

  1. https://www.librec.net/datasets.html.

  2. https://www.tensorflow.org/.

References

  1. Ahn K, Lee K, Cha H, Suh C (2018) Binary rating estimation with graph side information. In: Advances in neural information processing systems, pp 4272–4283

  2. Dong M, Yuan F, Yao L, Wang X, Xu X, Zhu L (2020) Trust in recommender systems: A deep learning perspective. arXiv preprint arXiv:2004.03774

  3. Dong X, Yu L, Wu Z, Sun Y, Yuan L, Zhang F (2017) A hybrid collaborative filtering model with deep structure for recommender systems. In: Thirty-First AAAI Conference on Artificial Intelligence

  4. Fan W, Li Q, Cheng M (2018) Deep modeling of social relations for recommendation. In: Thirty-Second AAAI Conference on Artificial Intelligence

  5. Fan W, Ma Y, Li Q, He Y, Zhao E, Tang J, Yin D (2019) Graph neural networks for social recommendation. In: The World Wide Web Conference, pp. 417–426. ACM

  6. Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y (2014) Generative adversarial nets. In: Advances in neural information processing systems, pp. 2672–2680

  7. Guo G, Zhang J, Yorke-Smith N (2015) Trustsvd: collaborative filtering with both the explicit and implicit influence of user trust and of item ratings. In: Twenty-Ninth AAAI Conference on Artificial Intelligence. AAAI

  8. Han J, Moraga C (1995) The influence of the sigmoid function parameters on the speed of backpropagation learning. In: International Workshop on Artificial Neural Networks, pp. 195–201. Springer

  9. He X, He Z, Du X, Chua TS (2018) Adversarial personalized ranking for recommendation. In: The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval, pp. 355–364. ACM

  10. He X, Liao L, Zhang H, Nie L, Hu X, Chua TS (2017) Neural collaborative filtering. In: Proceedings of the 26th international conference on world wide web, pp. 173–182. International World Wide Web Conferences Steering Committee

  11. Jamali M, Ester M (2009) Trustwalker: a random walk model for combining trust-based and item-based recommendation. In: Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 397–406. ACM

  12. Jamali M, Ester M (2009) Using a trust network to improve top-n recommendation. In: Proceedings of the third ACM conference on Recommender systems, pp. 181–188. ACM

  13. Jamali M, Ester M (2010) A matrix factorization technique with trust propagation for recommendation in social networks. In: Proceedings of the fourth ACM conference on Recommender systems, pp. 135–142. ACM

  14. Koren Y (2008) Factorization meets the neighborhood: a multifaceted collaborative filtering model. In: Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 426–434. ACM

  15. LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436–444

    Article  Google Scholar 

  16. Ma H, Zhou D, Liu C, Lyu MR, King I (2011) Recommender systems with social regularization. In: Proceedings of the fourth ACM international conference on Web search and data mining, pp. 287–296. ACM

  17. Ma X, Lu H, Gan Z, Zeng J (2017) An explicit trust and distrust clustering based collaborative filtering recommendation approach. Electr Commerce Res Appl 25:29–39

    Article  Google Scholar 

  18. Massa P, Avesani P (2007) Trust-aware recommender systems. In: Proceedings of the 2007 ACM conference on Recommender systems, pp. 17–24. ACM

  19. Pan Y, He F, Yu H (2017) Trust-aware collaborative denoising auto-encoder for top-n recommendation. arXiv preprint arXiv:1703.01760

  20. Sedhain S, Menon AK, Sanner S, Xie L (2015) Autorec: Autoencoders meet collaborative filtering. In: Proceedings of the 24th International Conference on World Wide Web, pp. 111–112. ACM

  21. Strub F, Gaudel R, Mary J (2016) Hybrid recommender system based on autoencoders. In: Proceedings of the 1st Workshop on Deep Learning for Recommender Systems, pp. 11–16. ACM

  22. Vincent P, Larochelle H, Lajoie I, Bengio Y, Manzagol PA (2010) Stacked denoising autoencoders: learning useful representations in a deep network with a local denoising criterion. J Mach Learn Res 11:3371–3408

    MathSciNet  MATH  Google Scholar 

  23. Wang H, Xingjian S, Yeung DY (2016) Collaborative recurrent autoencoder: Recommend while learning to fill in the blanks. In: Advances in neural information processing systems, pp. 415–423

  24. Wang Z, Gao M, Wang X, Yu J, Wen J, Xiong Q (2019) A minimax game for generative and discriminative sample models for recommendation. In: Pacific-Asia conference on knowledge discovery and data mining, pp. 420–431. Springer

  25. Wen Y, Guo L, Chen Z, Ma J (2018) Network embedding based recommendation method in social networks. In: Companion of the The Web Conference 2018 on The Web Conference 2018, pp. 11–12. International World Wide Web Conferences Steering Committee

  26. Wu Q, Zhang H, Gao X, He P, Weng P, Gao H, Chen G (2019) Dual graph attention networks for deep latent representation of multifaceted social effects in recommender systems. arXiv preprint arXiv:1903.10433

  27. Wu Y, DuBois C, Zheng AX, Ester M (2016) Collaborative denoising auto-encoders for top-n recommender systems. In: Proceedings of the Ninth ACM international conference on web search and data mining, pp. 153–162. ACM

  28. Yang B, Lei Y, Liu J, Li W (2017) Social collaborative filtering by trust. IEEE Trans Pattern Anal Mach Intell 39(8):1633–1647

    Article  Google Scholar 

  29. Yu Y, Gao Y, Wang H, Wang R (2018) Joint user knowledge and matrix factorization for recommender systems. World Wide Web 21(4):1141–1163

    Article  Google Scholar 

  30. Yuan F, Yao L, Benatallah B (2019) Adversarial collaborative neural network for robust recommendation. In: Proceedings of the 42th International ACM SIGIR conference on Research and Development in Information Retrieval

  31. Zhang C, Yu L, Wang Y, Shah C, Zhang X (2017) Collaborative user network embedding for social recommender systems. In: 17th SIAM International Conference on Data Mining, SDM 2017, pp 381–389. Society for Industrial and Applied Mathematics Publications

  32. Zhao Z, Yang Q, Lu H, Weninger T, Cai D, He X, Zhuang Y (2017) Social-aware movie recommendation via multimodal network learning. IEEE Trans Multimed 20(2):430–440

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. University of New South Wales, Sydney, Australia

    Manqing Dong & Lina Yao

  2. University of Technology Sydney, Sydney, Australia

    Xianzhi Wang

  3. Data 61, CSIRO, Sydney, Australia

    Xiwei Xu & Liming Zhu

Authors
  1. Manqing Dong
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Lina Yao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xianzhi Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xiwei Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Liming Zhu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Manqing Dong.

Ethics declarations

Conflict of interest

University of New South Wales; University of Technology Sydney; Data61, CSIRO.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Dong, M., Yao, L., Wang, X. et al. Adversarial dual autoencoders for trust-aware recommendation. Neural Comput & Applic 35, 13065–13075 (2023). https://doi.org/10.1007/s00521-021-05722-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-021-05722-3

Keywords

Search

Navigation